CAMPI is a multidisciplinary laboratory that aims at facilitating the match between specific needs from multiple domains of industrial innovation and opportunities provided by the different declinations of Artificial Intelligence and Data Science, now enabled by emerging technologies. For this purpose, CAMPI carries out research, project, and higher education activities developing new methods and tools for the proper use of data and IT in multiple application and industrial contexts. Members of CAMPI cooperate to stimulate both technology and methodological transfer, adapting formal models from applied mathematics, computer science, and theoretical physics in applied scenarios, through a collaborative effort that also includes joint projects with other research organizations, companies, and institutions.
CAMPI includes professors and researchers from multiple scientific domains, providing a common knowledge base to address
- Mathematics (both theoretic and applied)
- Computer Science
- Decision Sciences and Economics
- Information Engineering
- Management Engineering
For more information please contact: Mario Angelelli (firstname.lastname@example.org)
- Università del Piemonte Orientale
- Framework convention with Inmatica S.p.A. for joint research activities on the topic “Artificial Intelligence and Sport Management 4.0” (A.I&S.M.4.0) aimed at the definition of methods and tools powered by Artificial Intelligence and for Image/video processing for match analysis.
- Angelelli, M., Konopelchenko, B. (2021). Entropy driven transformations of statistical hypersurfaces. Reviews in Mathematical Physics 33(02): 2150001.
- Corallo, A., Fortunato, L., Massafra, A., Pasca, P., Angelelli, M., Hobbs, M., Al-Nasser, A.D., Al-Omari, A.I., Ciavolino, E. (2020). Sentiment analysis of expectation and perception of MILANO EXPO2015 in twitter data: a generalized cross entropy approach. Soft Computing 24(18): 13597-13607. (DOI:10.1007/s00500-019-04368-7)
- Corallo, A., Lazoi, M., & Striani, F. (2020). Process mining and industrial applications: A systematic literature review. Knowledge and Process Management 27(3): 225-233.
- Ventruto, F. Pulimeno, M., Cafaro, M. & Epicoco, I. (2020) On frequency estimation and detection of heavy hitters in data streams. Future Internet, 12(9): 158
(ISSN 1999-5903, DOI: 10.3390/fi12090158)
- Epicoco, I., Melle, C., Cafaro, M., Pulimeno, M., & Morleo, G. (2020) UDDSketch Accurate Tracking of Quantiles in Data Streams. IEEE Access 8: 147604-147617,
(ISSN: 2169-3536, DOI: 10.1109/ACCESS.2020.3015599)
- Ghobadi H., Spogli L., Alfonsi L., Cesaroni C., Cicone A., Linty N., Romano V., & Cafaro M. (2020) Disentangling ionospheric refraction and diffraction effects in GNSS raw phase through fast iterative filtering technique. GPS Solutions 24(85) (ISSN 1080-5370, DOI: 10.1007/s10291-020-01001-1)
- Damaceno J. G., Bolmgren, K., Bruno, J., De Franceschi, G., Mitchell, C., & Cafaro, M. (2020) GPS loss of lock statistics over Brazil during the 24th solar cycle. Advances in Space Research 66(2): 219-225
(ISSN 0273-1177, DOI 10.1016/j.asr.2020.03.041)
- Angelelli, M., Ciavolino, E., Pasca, P. (2019). Streaming generalized cross entropy. Soft Computing 24(18): 13837-13851.
- Angelelli, M. (2019). Complexity reduction for sign configurations through the KP II equation and its information-theoretic aspects. Journal of Mathematical Physics 60(7): 073511. (DOI: 10.1063/1.5086165)
- Angelelli, M., Arima, S., & Catalano, C. (2022). A Mixture Model for Multi-Source Cyber-Vulnerability Assessment. Accepted for SIS 2022 – The 51st Scientific Meeting of the Italian Statistical Society
- Angelelli, M., Catalano, C., Hill, D., Koshutanski, H., Pascarelli, C., & Rafferty, J. (2022). Reference Architecture Proposal for Secure Data Management in Mobile Health. Accepted for SpliTech 2022 – 7th International Conference on Smart and Sustainable Technologies 2022
- Ciavolino, E., Angelelli, M., & Blasi, F. S. (2021). Uncertainty and Factor Indeterminacy: Quality Perception of Bluefish Products. In: 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) (pp. 6-10). IEEE.
- Catalano, C., Afrune, P., Angelelli, M., Maglio, G., Striani, F., & Tommasi, F. (2021). Security Testing Reuse enhancing active cyber defence in Public Administration. In: Proceedings of Italian Conference on Cybersecurity (ITASEC 2021) (http://ceur- ws.org/Vol-2940/paper11.pdf)